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Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data

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Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data

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dc.contributor.author García-Cremades, Santi es_ES
dc.contributor.author Morales-García, Juan es_ES
dc.contributor.author Hernández-Sanjaime, Rocío es_ES
dc.contributor.author Martínez-España, Raquel es_ES
dc.contributor.author Bueno-Crespo, Andrés es_ES
dc.contributor.author Hernández-Orallo, Enrique es_ES
dc.contributor.author López-Espín, José J. es_ES
dc.contributor.author Cecilia-Canales, José María es_ES
dc.date.accessioned 2022-04-28T18:04:54Z
dc.date.available 2022-04-28T18:04:54Z
dc.date.issued 2021-07-26 es_ES
dc.identifier.issn 2045-2322 es_ES
dc.identifier.uri http://hdl.handle.net/10251/182270
dc.description.abstract [EN] We are witnessing the dramatic consequences of the COVID¿19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID¿19 pandemic to create a decision support system for policy¿makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14¿day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14¿day CI in scenarios with and without trend changes, reaching 0.93 R2, 4.16 RMSE and 1.08 MAE. es_ES
dc.description.sponsorship This work has been partially supported by the Spanish Ministry of Science and Innovation, under Grants RYC2018-025580-I, RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302 and a predoctoral contract by the Generalitat Valenciana and the European Social Fund under Grant ACIF/2018/219. es_ES
dc.language Inglés es_ES
dc.publisher Nature Publishing Group es_ES
dc.relation.ispartof Scientific Reports es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Computer science es_ES
dc.subject Scientific data es_ES
dc.subject Statistics es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1038/s41598-021-94696-2 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096384-B-I00/ES/SOLUCIONES PARA UNA GESTION EFICIENTE DEL TRAFICO VEHICULAR BASADAS EN SISTEMAS Y SERVICIOS EN RED/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//ACIF%2F2018%2F219/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Conselleria d'Educació, Investigació, Cultura i Esport de la Generalitat Valenciana//AICO%2F2020%2F302/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//AICO%2F2015%2F108//ANALISIS DE LA MOVILIDAD Y PERSISTENCIA DE LA INFORMACION EN REDES VEHICULARES. APLICACION A LA GESTION DE ACCIDENTES./ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//RTC-2017-6389-5-AR//PLANIFICACIÓN Y GESTIÓN DE RECURSOS HÍDRICOS A PARTIR DE ANÁLISIS DE DATOS DE IOT/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//RYC2018-025580-I//AYUDA ADICIONAL RAMON Y CAJAL/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AGENCIA ESTATAL DE INVESTIGACION//RTC2019-007159-5//DESARROLLO DE INFRAESTRUCTURAS IOT DE ALTAS PRESTACIONES CONTRA EL CAMBIO CLIMÁTICO BASADAS EN INTELIGENCIA ARTIFICIAL/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation García-Cremades, S.; Morales-García, J.; Hernández-Sanjaime, R.; Martínez-España, R.; Bueno-Crespo, A.; Hernández-Orallo, E.; López-Espín, JJ.... (2021). Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data. Scientific Reports. 11(1):1-16. https://doi.org/10.1038/s41598-021-94696-2 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1038/s41598-021-94696-2 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 16 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 1 es_ES
dc.identifier.pmid 34312455 es_ES
dc.identifier.pmcid PMC8313557 es_ES
dc.relation.pasarela S\443281 es_ES
dc.contributor.funder European Social Fund es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia es_ES
dc.contributor.funder Conselleria d'Educació, Investigació, Cultura i Esport de la Generalitat Valenciana es_ES
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